Field submerging duration date monitoring method and device, electronic equipment and storage medium

Abstract

The invention relates to a field flooding duration date monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring quantitative rainfall estimation data and an optical remote sensing satellite image of a target area; obtaining daily water body distribution data of the target area according to the quantitative rainfall estimation data; performing daily waterflooding area simulation on the target area by using the hydrological model to obtain daily waterflooding area data of the target area; performing field identification on the optical remote sensing satellite image to obtain field distribution data; according to the daily water body distribution data, the daily flooding area data and the field distribution data, the duration date of field flooding is obtained. According to the method, daily water distribution data is obtained based on quantitative rainfall estimation data. In combination with daily flooding area data simulated by a hydrological model, the duration date of flooding of each field can be determined.

Description

Technical Field

[0001] The present application relates to the field of geographic monitoring technology, and in particular to a method, device, electronic device and storage medium for monitoring the duration of field flooding. Background Art

[0002] In recent years, with the intensification of climate change, extreme weather events have become increasingly frequent. For example, in the mountainous areas of southern China, crops are often affected by typhoons, extreme rainfall, and flash floods. When crops are submerged and soaked for a certain number of days, widespread yield losses can occur. Satellite remote sensing technology can monitor the duration of farmland flooding and provide timely information on the distribution and extent of crop damage. This information can be used by relevant government departments to formulate disaster prevention and mitigation strategies, which is of great significance.

[0003] However, the existing remote sensing data replay cycle is as long as 5-12 days, and when disasters occur, the weather is cloudy and rainy, making it difficult to obtain effective observation data, making it difficult to monitor the duration of farmland flooding, which brings great difficulties to crop remote sensing monitoring and disaster assessment. Summary of the Invention

[0004] Based on this, the purpose of this application is to provide a method, device, electronic device and storage medium for monitoring the duration of field flooding, which can effectively monitor the duration of field flooding.

[0005] According to a first aspect of an embodiment of the present application, a method for monitoring the duration of field flooding is provided, comprising the following steps:

[0006] Obtain quantitative precipitation estimation data and optical remote sensing satellite images of the target area;

[0007] Based on the quantitative precipitation estimation data, obtain the daily water distribution data of the target area;

[0008] Use the hydrological model to simulate the daily flooded area of ​​the target area and obtain the daily flooded area data of the target area;

[0009] Identify fields on optical remote sensing satellite images to obtain field distribution data;

[0010] Based on the daily water body distribution data, daily flooded area data and field distribution data, the duration date of field flooding is obtained.

[0011] According to a second aspect of an embodiment of the present application, a device for monitoring the duration of field flooding is provided, comprising:

[0012] A data acquisition module is used to obtain quantitative precipitation estimation data and optical remote sensing satellite images of the target area;

[0013] Daily water distribution data acquisition module, used to obtain daily water distribution data of the target area based on quantitative precipitation estimation data;

[0014] A daily flooded area data acquisition module is used to simulate the daily flooded area of ​​the target area using a hydrological model to obtain the daily flooded area data of the target area;

[0015] The field distribution data acquisition module is used to identify fields using optical remote sensing satellite images and obtain field distribution data;

[0016] The duration date obtaining module is used to obtain the duration date of field flooding based on daily water body distribution data, daily flooded area data and field distribution data.

[0017] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the method of the first aspect.

[0018] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the steps of the method of the first aspect are implemented.

[0019] The embodiments of the present application obtain quantitative precipitation estimation data and optical remote sensing satellite imagery for a target area; obtain daily water distribution data for the target area based on the quantitative precipitation estimation data; simulate daily flooded areas in the target area using a hydrological model to obtain daily flooded area data for the target area; identify fields on the optical remote sensing satellite imagery to obtain field distribution data; and obtain the duration of field flooding based on the daily water distribution data, daily flooded area data, and field distribution data. The present application obtains daily water distribution data based on quantitative precipitation estimation data. Combined with the daily flooded area data simulated by the hydrological model, the duration of flooding for each field can be determined.

[0020] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application.

[0021] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for monitoring the duration of field flooding provided in one embodiment of the present application;

[0023] Figure 2 A structural block diagram of a device for monitoring the duration of field flooding provided in one embodiment of the present application;

[0024] Figure 3 A schematic block diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0026] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] The terms used in the embodiments of the present application are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present application. The singular forms "a," "the," and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0028] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0029] In addition, in this application, unless otherwise specified, "plurality" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0030] See also Figure 1 , which is a flow chart of a method for monitoring the duration of field flooding provided by an embodiment of the present application. The method for monitoring the duration of field flooding provided by an embodiment of the present application comprises the following steps:

[0031] S10: Obtain quantitative precipitation estimation data and optical remote sensing satellite images of the target area.

[0032] The target area is where the duration of field flooding is monitored. Quantitative Precipitation Estimation (QPE) data is obtained through observational methods such as radar and satellites. It is used to understand precipitation conditions and monitor precipitation intensity, range, area, and trends, providing important initial field data for flood monitoring. Optical remote sensing satellite imagery is images of the Earth's surface captured by optical remote sensing satellites orbiting the Earth.

[0033] In the embodiment of the present application, daily QPE data of the target area can be obtained from the National Meteorological Administration, the Hydrological Management Center, or a meteorological data platform. Optical remote sensing satellite images of the target area can be obtained from the National Space Administration or a remote sensing satellite platform.

[0034] S20: Obtain daily water distribution data for the target area based on the quantitative precipitation estimation data.

[0035] Among them, daily water body distribution data includes but is not limited to daily water body location data and water body types. Water body types include permanent water bodies, seasonal water bodies and temporary water bodies. Permanent water bodies include but are not limited to rivers, lakes, seas and fish ponds. Seasonal water bodies include paddy fields. Temporary water bodies include areas flooded by heavy rain.

[0036] In an embodiment of the present application, the quantitative precipitation estimation data can be converted into synthetic aperture radar data or Gaofen-3 data, and the daily water distribution data of the target area can be extracted from the synthetic aperture radar data or Gaofen-3 data.

[0037] S30: simulating the daily flooded area of ​​the target area using a hydrological model to obtain daily flooded area data of the target area.

[0038] The hydrological model refers to an approximate scientific model that generalizes complex hydrological phenomena and processes using simulation methods. Daily flooded area data includes but is not limited to the location data and water depth of the flooded areas.

[0039] In an embodiment of the present application, input data of the hydrological model is collected, the input data is preprocessed, and the preprocessed input data is input into the hydrological model to simulate the daily flooded area of ​​the target area to obtain the daily flooded area data of the target area.

[0040] S40: Perform field identification on the optical remote sensing satellite image to obtain field distribution data.

[0041] The field distribution data includes the location data of the fields.

[0042] In the embodiment of the present application, a field identification model is used to identify fields in optical remote sensing satellite images to obtain the location data of each field in the target area. The field identification model is obtained by training a deep learning neural network.

[0043] S50: Obtain the duration of field flooding according to the daily water body distribution data, the daily flooded area data, and the field distribution data.

[0044] In this embodiment of the present application, whether a field is flooded can be determined based on daily water body distribution data and field distribution data. Whether a field is flooded can also be determined based on daily flooded area data and field distribution data. After a field is flooded, the number of days the field has been flooded is determined to obtain the duration of the flooding.

[0045] Using the embodiments of the present application, quantitative precipitation estimation data and optical remote sensing satellite imagery are obtained for a target area; daily water distribution data for the target area is obtained based on the quantitative precipitation estimation data; daily flooded area data for the target area is simulated using a hydrological model; field identification is performed on the optical remote sensing satellite imagery to obtain field distribution data; and the duration of field flooding is determined based on the daily water distribution data, daily flooded area data, and field distribution data. This application obtains daily water distribution data based on quantitative precipitation estimation data. Combined with the daily flooded area data simulated by the hydrological model, the duration of flooding for each field can be determined.

[0046] In one embodiment, before step S20, step S201 is included, which is specifically as follows:

[0047] S201: Preprocessing the quantitative precipitation estimation data; the preprocessing includes but is not limited to radiation correction, atmospheric correction, and geometric correction.

[0048] Among them, radiation correction refers to the process of correcting systematic and random radiation distortion or aberration caused by external factors, data acquisition and transmission systems, in order to eliminate or correct image distortion caused by radiation errors.

[0049] Atmospheric correction means that the total radiance of ground targets measured by the sensor does not reflect the true reflectance of the surface. It includes radiation errors caused by atmospheric absorption, especially scattering. Atmospheric correction is the process of eliminating these radiation errors caused by atmospheric influences to invert the true surface reflectance of the ground object.

[0050] Geometric correction refers to the process of eliminating or correcting the geometric errors of remote sensing images.

[0051] In the embodiment of the present application, radiation correction, atmospheric correction, and geometric correction are performed on the quantitative precipitation estimation data, which can improve the accuracy and reliability of the quantitative precipitation estimation data.

[0052] In one embodiment, step S20 includes steps S21 to S22, which are specifically as follows:

[0053] S21: Input the quantitative precipitation estimation data into the trained image generation model to obtain daily synthetic aperture radar observation images.

[0054] Considering that the replay period of the Sentinel-1 satellite is about one week, it is impossible to obtain daily synthetic aperture radar observation images. Therefore, an image generation model is used to generate daily synthetic aperture radar observation images.

[0055] Among them, the trained image generation model is the Stable Diffusion image generation model. The Stable Diffusion image generation model can greatly reduce video memory usage and computational complexity by performing forward diffusion and reverse generation processes in a low-dimensional Latent latent space.

[0056] In the embodiment of the present application, the quantitative precipitation estimation data is used as the input of the trained image generation model, which then outputs daily synthetic aperture radar observation images with a spatial resolution of 10 meters.

[0057] S22: Perform polarization processing on daily synthetic aperture radar observation images to obtain daily water distribution data of the target area.

[0058] The polarization processing includes but is not limited to HV cross-polarization and VH cross-polarization.

[0059] In an embodiment of the present application, daily water distribution data of the target area can be obtained by performing HV cross-polarization or VH cross-polarization on daily synthetic aperture radar observation images.

[0060] In one embodiment, before step S21, steps S211 to S212 are included, specifically as follows:

[0061] S211: Obtain sample quantitative precipitation estimation data and sample synthetic aperture radar observation images;

[0062] S212: The sample quantitative precipitation estimation data is used as input, and the sample synthetic aperture radar observation image is used as output, and the data are input into the image generation model for training and learning to obtain a trained image generation model.

[0063] In this embodiment of the present application, sample quantitative precipitation estimation data and sample synthetic aperture radar observation images are pre-collected and input into an image generation model to obtain a prediction result. The prediction result and the sample synthetic aperture radar observation image are then input into a preset loss function to obtain a loss function value. Based on the loss function value, the image generation model is iteratively trained until the loss function value falls below a preset threshold, thereby obtaining a trained image generation model.

[0064] In one embodiment, the daily flooded area data includes daily flooded depth and daily flooded range. Step S30 includes steps S31 to S32, which are specifically as follows:

[0065] S31: Acquire ground meteorological data, topographic data, river data, soil property data, land use type data, and field parameters of the target area;

[0066] In the embodiments of the present application, ground meteorological data include precipitation, temperature, humidity, and wind speed data, which can be obtained through meteorological station observations or reanalysis data. The terrain data is digital elevation model data, specifically, SRTM terrain data. River channel data includes river width, depth, and slope data, which can be obtained through GIS technology. Soil property data includes soil permeability and water holding capacity data, which can be obtained from the Resource and Environmental Science and Data Center. Land use type data can use ESA WorldCover data. Field parameters include crop type, growth stage, and planting density, which can be analyzed and obtained from remote sensing data.

[0067] S32: Inputting ground meteorological data, topographic data, river data, soil property data, land use type data and field parameters into the hydrological model to obtain daily flooding depth and daily flooding range.

[0068] In an embodiment of the present application, ground meteorological data, terrain data, river data, soil property data, land use type data and field parameters are used as inputs of a hydrological model, and the hydrological model is run to simulate daily flooded areas. The output of the hydrological model includes daily flooding depth and daily flooding range.

[0069] In one embodiment, step S40 includes step S41, which is specifically as follows:

[0070] S41: Inputting the optical remote sensing satellite image into the trained field recognition model to obtain field distribution data;

[0071] Among them, training the field recognition model includes:

[0072] Perform field digitization and sample enhancement processing on the optical remote sensing satellite sample image to obtain the processed optical remote sensing satellite sample image;

[0073] The processed optical remote sensing satellite sample images are used to outline the field boundaries and obtain the field sample distribution data;

[0074] The processed optical remote sensing satellite sample images are used as input, and the field sample distribution data is used as output. They are input into the Transformer model for training and learning to obtain a trained field recognition model.

[0075] Among them, the Transformer model includes an encoder and a decoder, and the self-attention mechanism is introduced in the encoder.

[0076] In an embodiment of the present application, a large number of optical remote sensing satellite sample images are collected in advance, and then for each optical remote sensing satellite sample image, the field boundaries in the sample image are manually outlined one by one to obtain field sample distribution data. The optical remote sensing satellite sample image is used as input, and the field sample distribution data is used as output, which are input into the Transformer model for training and learning, thereby obtaining a trained field recognition model.

[0077] In one embodiment, step S50 includes steps S51 to S53, which are specifically as follows:

[0078] S51: Matching the positions of each field in the field distribution data with each water body in the daily water body distribution data, and when the field and the water body overlap, determining the field as the target field; or,

[0079] In the embodiment of the present application, when a field overlaps with a water body, it indicates that the field is submerged in water.

[0080] S52: Positionally matching each field in the field distribution data with each flooded area in the daily flooded area data. When a field and a flooded area overlap, determining the field as a target field; wherein the target field is a flooded field.

[0081] In the embodiment of the present application, when a field overlaps with a flooded area, it indicates that the field is flooded.

[0082] S53: Count the consecutive days that each field in the target area is a target field within a preset time period, and obtain the duration of flooding of each field.

[0083] The preset time period can be set manually according to actual needs, for example, 7 days or 1 month.

[0084] In the embodiment of the present application, the number of consecutive days that each field is flooded is counted to obtain the duration of the flooding of each field.

[0085] The following are embodiments of the apparatus of the present application, which can be used to execute the contents of the method in the embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the contents of the method in the embodiments of the present application.

[0086] See Figure 2 , which shows a schematic diagram of the structure of the field flooding duration monitoring device provided in an embodiment of the present application. The field flooding duration monitoring device 6 provided in an embodiment of the present application includes:

[0087] A data acquisition module 61 is used to acquire quantitative precipitation estimation data and optical remote sensing satellite images of a target area;

[0088] The daily water distribution data acquisition module 62 is used to obtain the daily water distribution data of the target area based on the quantitative precipitation estimation data;

[0089] The daily flooded area data acquisition module 63 is used to simulate the daily flooded area of ​​the target area using a hydrological model to obtain the daily flooded area data of the target area;

[0090] The field distribution data acquisition module 64 is used to perform field identification on the optical remote sensing satellite image to obtain field distribution data;

[0091] The duration date obtaining module 65 is used to obtain the duration date of field flooding based on the daily water body distribution data, the daily flooded area data and the field distribution data.

[0092] It should be noted that the field flooding duration monitoring device provided in the above embodiment, when executing the field flooding duration monitoring method, only uses the division of the above functional modules as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the field flooding duration monitoring device provided in the above embodiment and the field flooding duration monitoring method are based on the same concept. The implementation process is detailed in the method embodiment and will not be repeated here.

[0093] The following are device embodiments of the present application, which can be used to perform the content of the method in the embodiment of the present application. For details not disclosed in the device embodiments of the present application, please refer to the content of the method in the embodiment of the present application.

[0094] See also Figure 3The present application also provides an electronic device 300, which may be a computer, a mobile phone, a tablet computer, etc. In an exemplary embodiment of the present application, the electronic device 300 is a computer, which may include: at least one processor 301, at least one memory 302, at least one display, at least one network interface 303, a user interface 304, and at least one communication bus 305.

[0095] The user interface 304 is mainly used to provide an input interface for the user and obtain data input by the user. Optionally, the user interface can also include a standard wired interface or a wireless interface.

[0096] The network interface 303 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0097] The communication bus 305 is used to realize the connection and communication between these components.

[0098] Among them, the processor 301 may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and performs various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in the form of at least one hardware of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or more combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed by the display layer; and the modem is used to handle wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor and may be implemented separately through a chip.

[0099] Among them, the memory 302 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory may also be optionally at least one storage device located away from the aforementioned processor. As Figure 3 As shown, the memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and an operating application program.

[0100] The processor can be used to call the application of the field flooding duration monitoring method stored in the memory and specifically execute the method steps of the above-mentioned embodiment. The specific execution process can be found in the specific description shown in the embodiment, which will not be repeated here.

[0101] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0102] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for monitoring the duration of field flooding, characterized in that: The steps include: Obtain quantitative precipitation estimation data and optical remote sensing satellite images of the target area; Obtaining daily water distribution data for the target area based on the quantitative precipitation estimation data; Using a hydrological model to simulate the daily flooded area of ​​the target area to obtain daily flooded area data of the target area; Performing field identification on the optical remote sensing satellite image to obtain field distribution data; The duration of field flooding is obtained according to the daily water body distribution data, the daily flooded area data and the field distribution data.

2. The method for monitoring the duration of field flooding according to claim 1, characterized in that: The step of obtaining daily water distribution data of the target area based on the quantitative precipitation estimation data comprises: Inputting the quantitative precipitation estimation data into a trained image generation model to obtain daily synthetic aperture radar observation images; Polarization processing is performed on the daily synthetic aperture radar observation image to obtain daily water distribution data of the target area.

3. The method for monitoring the duration of field flooding according to claim 2, characterized in that: Before the step of inputting the quantitative precipitation estimation data into the trained image generation model to obtain daily synthetic aperture radar observation images, the method includes: Obtain sample quantitative precipitation estimation data and sample synthetic aperture radar observation images; The sample quantitative precipitation estimation data is used as input, and the sample synthetic aperture radar observation image is used as output, which are input into an image generation model for training and learning to obtain a trained image generation model.

4. The method for monitoring the duration of field flooding according to claim 1, characterized in that: The daily flooded area data includes daily flooded depth and daily flooded range; The step of simulating the daily flooded area of ​​the target area using a hydrological model to obtain daily flooded area data of the target area includes: Acquiring ground meteorological data, topographic data, river data, soil property data, land use type data, and field parameters of the target area; The ground meteorological data, the topographic data, the river data, the soil property data, the land use type data and the field parameters are input into the hydrological model to obtain the daily flooding depth and the daily flooding range.

5. The method for monitoring the duration of field flooding according to claim 1, characterized in that: The step of performing field identification on the optical remote sensing satellite image to obtain field distribution data includes: Inputting the optical remote sensing satellite image into a trained field identification model to obtain field distribution data; The training of the field identification model includes: Perform field digitization and sample enhancement processing on the optical remote sensing satellite sample image to obtain the processed optical remote sensing satellite sample image; outlining field boundaries on the processed optical remote sensing satellite sample images to obtain field sample distribution data; The processed optical remote sensing satellite sample image is used as input, and the field sample distribution data is used as output, which are input into the Transformer model for training and learning to obtain a trained field recognition model.

6. The method for monitoring the duration of field flooding according to any one of claims 1 to 5, characterized in that: The step of obtaining the duration of field flooding based on the daily water body distribution data, the daily flooded area data, and the field distribution data comprises: Matching the positions of each field in the field distribution data with each water body in the daily water body distribution data, and determining that the field is a target field when the field and the water body overlap; or Positionally matching each field in the field distribution data with each flooded area in the daily flooded area data, and determining that the field is a target field when there is overlap between the field and the flooded area; wherein the target field is a flooded field; The number of consecutive days that each field in the target area is the target field within a preset time period is counted to obtain the duration of flooding of each field.

7. The method for monitoring the duration of field flooding according to any one of claims 1 to 5, characterized in that: Before the step of obtaining daily water distribution data of the target area based on the quantitative precipitation estimation data, the method includes: The quantitative precipitation estimation data is preprocessed; the preprocessing includes but is not limited to radiation correction, atmospheric correction, and geometric correction.

8. A device for monitoring the duration of field flooding, characterized in that: include: A data acquisition module is used to obtain quantitative precipitation estimation data and optical remote sensing satellite images of the target area; A daily water body distribution data acquisition module is used to obtain daily water body distribution data of the target area based on the quantitative precipitation estimation data; A daily flooded area data acquisition module is used to simulate the daily flooded area of ​​the target area using a hydrological model to obtain daily flooded area data of the target area; a field distribution data acquisition module, configured to perform field identification on the optical remote sensing satellite image to obtain field distribution data; The duration date obtaining module is used to obtain the duration date of field flooding based on the daily water body distribution data, the daily flooded area data and the field distribution data.

9. An electronic device, characterized in that: include: A processor and a memory; wherein the memory stores a computer program, the computer program being adapted to be loaded by the processor and executing the steps of the method for monitoring the duration of field flooding as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for monitoring the duration of field flooding as claimed in any one of claims 1 to 7 are implemented.

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